Trade-offs exist between the point of early detection and the future cost of controlling any invasive species. Finding optimal levels of early detection, with post-border active surveillance, where time, space and randomness are explicitly considered, is computationally challenging. We use a stochastic programming model to find the optimal level of surveillance and predict damages, easing the computational challenge by combining a sample average approximation (SAA) approach and parallel processing techniques. The model is applied to the case of Asian Papaya Fruit Fly (PFF), a highly destructive pest, in Queensland, Australia. To capture the non-linearity in PFF spread, we use an agent-based model (ABM), which is calibrated to a highly detailed land-use raster map (50 m × 50 m) and weather-related data, validated against a historical outbreak. The combination of SAA and ABM sets our work apart from the existing literature. Indeed, despite its increasing popularity as a powerful analytical tool, given its granularity and capability to model the system of interest adequately, the complexity of ABM limits its application in optimizing frameworks due to considerable uncertainty about solution quality. In this light, the use of SAA ensures quality in the optimal solution (with a measured optimality gap) while still being able to handle large-scale decision-making problems. With this combination, our application suggests that the optimal (economic) trap grid size for PFF in Queensland is ˜0.7 km, much smaller than the currently implemented level of 5 km. Although the current policy implies a much lower surveillance cost per year, compared with the $2.08 million under our optimal policy, the expected total cost of an outbreak is $23.92 million, much higher than the optimal policy of roughly $7.74 million.

中文翻译：

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针对生物入侵的最佳监测：应用于基于代理的传播模型的样本平均近似方法

在早期发现点和控制任何入侵物种的未来成本之间存在权衡。在明确考虑时间、空间和随机性的情况下，通过边界后主动监视找到最佳的早期检测水平在计算上具有挑战性。我们使用随机规划模型来找到最佳监控水平并预测损害，通过结合样本平均近似 (SAA) 方法和并行处理技术来缓解计算挑战。该模型应用于澳大利亚昆士兰州的一种极具破坏性的害虫亚洲木瓜果蝇 (PFF) 的案例。为了捕捉 PFF 传播的非线性，我们使用基于代理的模型 (ABM)，该模型已校准为高度详细的土地利用栅格图 (50 m × 50 m) 和天气相关数据，并根据历史数据进行验证爆发。SAA 和 ABM 的结合使我们的工作有别于现有的文献。事实上，尽管它作为一种强大的分析工具越来越受欢迎，但考虑到它的粒度和充分建模感兴趣系统的能力，由于解决方案质量的相当大的不确定性，ABM 的复杂性限制了其在优化框架中的应用。有鉴于此，SAA 的使用确保了最优解决方案的质量（具有可测量的最优差距），同时仍然能够处理大规模的决策问题。通过这种组合，我们的应用表明昆士兰州 PFF 的最佳（经济）陷阱网格尺寸约为 0.7 公里，远小于目前实施的 5 公里水平。尽管与我们的最佳政策下的 208 万美元相比，目前的政策意味着每年的监控成本要低得多，